{"id":"W2951052484","doi":"10.48550/arxiv.1405.0189","title":"On Hardness of Jumbled Indexing","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Omega; Search engine indexing; Combinatorics; Substring; Preprocessor; Matching (statistics); Mathematics; Sigma; Constant (computer programming); Pattern matching; Computer science; Algorithm; Data structure; Statistics; Information retrieval; Physics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003107878,0.001730947,0.003999794,0.001717171,0.00448586,0.008787391,0.00604069,0.00378139,0.02204918],"category_scores_gemma":[0.02523582,0.0015303,0.003464487,0.0052604,0.004707358,0.02366571,0.007426271,0.008362284,0.006969245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004128906,"about_ca_system_score_gemma":0.003850113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005816766,"about_ca_topic_score_gemma":0.00401406,"domain_scores_codex":[0.9914696,0.001661307,0.0006177101,0.002305074,0.002142885,0.001803542],"domain_scores_gemma":[0.9604109,0.02747246,0.001606785,0.007749612,0.00132294,0.001437359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.006979579,0.001704504,0.009689415,0.003913254,0.0004238186,0.001266335,0.003879489,0.1066424,0.02230369,0.3863857,0.2516546,0.2051572],"study_design_scores_gemma":[0.000591527,0.0002376781,0.00247296,0.0002008752,0.0001774514,0.0009502394,0.0008128154,0.1727474,0.007745882,0.7899769,0.02393686,0.0001494864],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4653978,0.008706328,0.3200603,0.03696593,0.001065388,0.0007145911,0.02380725,0.01638199,0.1269005],"genre_scores_gemma":[0.8247285,0.003134609,0.1045382,0.005891817,0.001730491,0.0007715646,0.02033227,0.003388589,0.03548405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02204918,"threshold_uncertainty_score":0.07376188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0630169761731807,"score_gpt":0.1861705511471469,"score_spread":0.1231535749739662,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}